Enhanced mass-spectrometry-based approaches for in-depth profiling of the cancer extracellular matrix

增强型基于质谱的方法，用于深入分析癌症细胞外基质

基本信息

批准号：
10493806
负责人：
Yu Gao
金额：
$ 21.11万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10493806
关键词：
Acute Adopted Advanced Malignant Neoplasm Amino Acid Sequence Architecture Area Benchmarking Biochemical Categories Cell physiology Cessation of life Communities Complex Computer software DNA Sequence Alteration Data Data Set Databases Development Digestion Disease Early Diagnosis Enzymes Extracellular Matrix Extracellular Matrix Proteins Extracellular Structure Fibroblasts Future Generations Genetic study Growth Factor Individual Knowledge Malignant Neoplasms Maps Mass Spectrum Analysis Mediating Metabolic Methods Modality Modeling Neoplasm Metastasis Normal tissue morphology Outcome Pathway interactions Patient-Focused Outcomes Peptide Hydrolases Peptide Mapping Peptides Performance Play Post-Translational Protein Processing Preparation Prognostic Marker Property Protein Denaturation Protein Isoforms Proteins Proteolysis Proteomics Protocols documentation Publishing Reagent Research Research Personnel Resistance development Resources Role Sampling Signal Transduction Structural Protein Structure Techniques Technology Time Tissues Tumor Tissue Tumor stage United States Vial device Visualization Work anticancer research base cancer proteomics cell motility chemical property cost crosslink experimental study in silico innovation insight instrumentation knowledge base matrigel medical specialties neoplastic cell new technology novel novel therapeutics predictive modeling predictive signature prevent protein complex protein data bank protein folding protein function protein structure searchable database targeted treatment therapeutic target three dimensional structure three-dimensional modeling tool translational potential tumor tumor microenvironment tumor progression

项目摘要

Project Summary Cancer has claimed over 600,000 lives in 2020 in the United States. A better understanding of the mechanisms underlying cancer progression has led to the development of early detection strategies and novel treatment modalities that have contributed to the decrease in cancer-related deaths observed for the past few decades. Yet, cancer remains a deadly disease. There is thus an acute need to identify new cancer vulnerabilities. This will require exploring understudied aspects of cancers, which requires the development of novel technologies. One understudied aspect of cancer is the extracellular matrix (ECM). The ECM is a complex meshwork of proteins providing architectural support and biochemical signals critical for cellular functions required for tumor progression. Overcoming technical challenges posed by largely insoluble ECM proteins, we previously devised a proteomic pipeline specifically geared towards ECM proteins and showed that the tumor ECM is composed of 200+ distinct proteins. We further identified ECM signatures predictive of patient outcome and novel ECM proteins playing functional roles in cancer progression. The ECM thus represents an important reservoir of potential prognostic biomarkers and therapeutic targets. However, the ECM has many more secrets to reveal. For example, ECM proteins exist in various isoforms and are extensively post-translationally modified, yet, we do not know which proteoforms are present in the tumor ECM. ECM protein structure and the architecture of the ECM meshwork is key to mediate function, yet, very little is known about ECM protein folding and its impact on protein functions. Since proteomics relies on the generation of peptides from protein via proteolysis and protein identification via database search, we propose that enhancing these steps will provide a more complete picture of the cancer ECM and significantly advance cancer research. Here, we propose to use in-silico modeling to define the optimal cleavage conditions to achieve near-complete coverage of ECM protein sequences (Aim 1). Standard proteomic protocols rely on protein denaturation prior to protein digestion. Yet, we know that many ECM functions are governed by its architecture. We thus propose to perform native ECM digestion to gain insights into the structure of individual proteins, and the secondary and tertiary structures of the ECM meshwork (Aim 2). To facilitate ECM research, we have previously developed a searchable database, MatrisomeDB, compiling ECM proteomic dataset. Here, we propose to enhance the content and functionalities of MatrisomeDB to include our new prediction model and a new tool to the visualize sequence coverage on 3D models of ECM proteins predicted by Google’s AlphaFold (Aim 3). Our technology, offering substantial improvements over conventional proteomic approaches, targets the unmet technical need to profile, with deep coverage and high sensitivity, the protein composition of the tumor ECM. When deployed it will significantly lower the technical barrier for other researchers to study the ECM, which will have a transformative impact on cancer research.

项目概要 2020 年，癌症在美国夺去了超过 60 万人的生命。更好地了解其机制。潜在的癌症进展导致了早期检测策略和新治疗方法的发展过去几十年来观察到的导致癌症相关死亡人数减少的方式。然而，癌症仍然是一种致命的疾病，因此迫切需要识别新的癌症脆弱性。将需要探索癌症的未充分研究的方面，这需要开发新技术。癌症的一个未被充分研究的方面是细胞外基质 (ECM)。ECM 是一个复杂的网络。蛋白质提供对肿瘤所需的细胞功能至关重要的结构支持和生化信号为了克服主要不溶性 ECM 蛋白进展带来的技术挑战，我们之前设计了专门针对 ECM 蛋白的蛋白质组管道，表明肿瘤 ECM 由以下组成我们进一步鉴定了 200 多种不同的蛋白质，可预测患者的结果和新型 ECM。因此，ECM 是癌症进展中发挥重要作用的蛋白质。然而，ECM 还有更多秘密有待揭示。例如，ECM 蛋白以多种亚型存在，并且通常经过翻译后修饰，然而，我们不知道肿瘤 ECM 蛋白结构和结构中存在哪些蛋白质形式。 ECM 网络是介导功能的关键，然而，人们对 ECM 蛋白折叠及其对细胞的影响知之甚少。由于蛋白质组学依赖于通过蛋白水解和蛋白质从蛋白质产生肽。通过数据库搜索进行识别，我们建议增强这些步骤将提供更完整的图片癌症 ECM 并显着推进癌症研究在这里，我们建议使用计算机模型来进行。定义最佳切割条件以实现 ECM 蛋白质序列几乎完全覆盖（目标 1）。标准蛋白质组学方案依赖于蛋白质消化之前的蛋白质去饱和。 ECM 功能由其架构控制，因此我们建议执行本机 ECM 消化以获得收益。深入了解单个蛋白质的结构以及 ECM 网络的二级和三级结构（目标 2）为了促进 ECM 研究，我们之前开发了一个可搜索的数据库，MatrisomeDB，编译 ECM 蛋白质组数据集在这里，我们建议增强 MatrisomeDB 的内容和功能。包括我们的新预测模型和新工具，用于可视化 ECM 3D 模型上的序列覆盖范围谷歌的 AlphaFold（目标 3）预测的蛋白质，与我们的技术相比，提供了实质性的改进。传统的蛋白质组学方法，针对未满足的技术需求进行分析，具有深度覆盖和高敏感性，肿瘤 ECM 的蛋白质成分，部署后会显着降低技术水平。其他研究人员研究 ECM 的障碍，这将对癌症研究产生变革性影响。